Economic productivity loss due to breast cancer in Norway –  a case control study using the human capital approach

Dahl, Fredrik A.; Benth, Jūratė Šaltytė; Aas, Eline; Lurås, Hilde

doi:10.5617/njhe.2981

Cited by 5 publications

(2 citation statements)

References 9 publications

(13 reference statements)

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“…For example, some studies have included death of a worker, and thus future wages, as productivity loss, which would place the present value calculation of productivity loss greater than $200,000. [26][27][28] In this study, we estimated productivity loss in a manner similar to the approach used by Yin et al 5 in their study of BC progression and workplace productivity. They calculated lost wages at two time periods, estimating productivity loss between $24,166 and $30,666, similar to our calculation of $37,445.…”

Section: Discussionmentioning

confidence: 99%

Estimating Productivity Loss from Breast and Non–Small-Cell Lung Cancer among Working-Age Patients and Unpaid Caregivers: A Survey Study Using the Multiplier Method

Chiu¹,

MacEwan²,

May³

et al. 2022

MDM Policy & Practice

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Background. Traditional approaches to capturing health-related productivity loss (e.g., the human capital method) focus only on the foregone wages of affected patients, overlooking the losses caregivers can incur. This study estimated the burden of productivity loss among breast cancer (BC) and non–small-cell lung cancer (NSCLC) patients and individuals caring for such patients using an augmented multiplier method. Design. A cross-sectional survey of BC and NSCLC patients and caregivers measured loss associated with time absent from work (absenteeism) and reduced effectiveness (presenteeism). Respondents reported pre- and postcancer diagnosis income, hours worked, and time to complete tasks. Exploratory multivariable analyses examined correlations between respondents’ clinical/demographic characteristics—including industry of employment—and postdiagnosis productivity. Results. Of 204 patients (104 BC, 100 NSCLC) and 200 caregivers (100 BC, 100 NSCLC) who completed the survey, 319 participants (162 BC, 157 NSCLC) working ≥40 wk/y prediagnosis were included in the analysis. More than one-third of the NSCLC (33%) and BC (43%) patients left the workforce postdiagnosis, whereas only 15% of caregivers did. The traditional estimate for the burden of productivity loss was 66% lower on average than the augmented estimate (NSCLC patients: 60%, BC patients: 69%, NSCLC caregivers: 59%, and BC caregivers: 73%). Conclusions. Although patients typically experience greater absenteeism, productivity loss incurred by caregivers is also substantial. Failure to account for such impacts can result in substantial underestimation of productivity gains novel cancer treatments may confer by enabling patients and caregivers to remain in the workforce longer. Our results underscore the importance of holistic approaches to understanding this impact on both patients and their caregivers and accounting for such considerations when making decisions about treatment and treatment value. Highlights Cancer can have a profound impact on productivity. This study demonstrates how the disease affects not only patients but also the informal or unpaid individuals who care for patients. An augmented approach to calculating health-related productivity loss suggests that productivity impacts are much larger than previously understood. A more comprehensive understanding of the economic burden of cancer for both patients and their caregivers suggests the need for more support in the workplace for these individuals and a holistic approach to accounting for these impacts in treatment decision making.

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Section: Discussionmentioning

confidence: 99%

Estimating Productivity Loss from Breast and Non–Small-Cell Lung Cancer among Working-Age Patients and Unpaid Caregivers: A Survey Study Using the Multiplier Method

Chiu¹,

MacEwan²,

May³

et al. 2022

MDM Policy & Practice

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“…Put another way, if such studies were undertaken separately for all diseases, the sum of income loss across these studies would (likely greatly) exceed the actual income loss from all diseases considered together. Even studies from Scandinavia, with population-wide disease registers linked to taxation or employment data, have also focused on single diseases such as diabetes [ 10 ], breast cancer [ 11 ], and injury [ 12 ]. We are aware of only one study that has considered multiple diseases simultaneously in estimating productivity loss for a national cohort [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data

et al. 2021

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Background Reducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease). Methods and findings We used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level. Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026). The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to “healthy” person time. Conclusions In this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.

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Labour productivity loss caused by premature deaths associated with breast cancer: results from Spain over a 10-year period

Oliva‐Moreno

Peña-Longobardo

2018

Breast Cancer Res Treat

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Economic productivity loss due to breast cancer in Norway – a case control study using the human capital approach

Abstract: Abstract:We estimate the productivity loss attributable to breast cancer

Cited by 5 publications

References 9 publications

Estimating Productivity Loss from Breast and Non–Small-Cell Lung Cancer among Working-Age Patients and Unpaid Caregivers: A Survey Study Using the Multiplier Method

Estimating Productivity Loss from Breast and Non–Small-Cell Lung Cancer among Working-Age Patients and Unpaid Caregivers: A Survey Study Using the Multiplier Method

Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data

Labour productivity loss caused by premature deaths associated with breast cancer: results from Spain over a 10-year period

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